Biomedical subjects

Ruth M Pfeiffer

Publications and source records attributed to Ruth M Pfeiffer.

10 recordsLinked to original sources

PubMed2003

CYP17 polymorphisms in relation to risks of prostate cancer and benign prostatic hyperplasia: a population-based study in China.

Because androgens likely play a key role in prostate growth and prostate cancer development, variants of genes involved in androgen biosynthesis may be related to prostate cancer risk. The enzyme P450c17alpha, encoded by the CYP17 gene, catalyzes the conversion of progesterone and pregnenolone into precursors of potent androgens. In the 5' promoter region of the CYP17 gene, a T (A1 allele) to C substitution (A2 allele) has been hypothesized to increase CYP17 gene expression, resulting in higher levels of androgens. To investigate a possible role of CYP17 in prostate diseases, we evaluated the risk of prostate cancer and benign prostatic hyperplasia (BPH) in relation to variation in CYP17 genotype in a population-based case-control study conducted in Shanghai, China. The study included 174 prostate cancer cases, 182 BPH cases and 274 population controls. We observed no statistically significant overall associations of CYP17 genotypes with prostate cancer risk, although associations of the A1/A1 (odds ratio (OR) =1.42, 95% confidence interval (CI) 0.83-2.48) and A1/A2 (OR 1.41, 95% CI 0.91-2.17) genotypes with prostate cancer were suggested. A similar association of the A1/A1 genotype with BPH was suggested. We found no associations of CYP17 genotypes with serum sex hormone levels or other biomarkers after correction for multiple comparisons. Large population-based studies are needed to clarify whether CYP17 plays a role in prostate cancer risk and whether genotype effects vary in different racial/ethnic and other subgroups.

Aged↗

PubMed2003

Human herpesvirus 8 infection and transfusion history in children with sickle-cell disease in Uganda.

BACKGROUND: Although human herpesvirus 8 (HHV-8), the etiologic agent for Kaposi's sarcoma, can be detected in peripheral blood, blood-borne transmission of this virus has not been demonstrated. We studied the association between HHV-8 seropositivity and transfusion history among children with sickle-cell disease in Uganda, where HHV-8 infection is common in blood donors. METHODS: We studied 600 children (aged 0-16 years) with sickle-cell disease at Mulago Hospital, Kampala, from November 2001 through April 2002. By design, about half had previously been transfused. HHV-8 serostatus was determined using enzyme-linked immunosorbent assays for antibodies against HHV-8 proteins K8.1 and orf 73. We used logistic regression to test for an association between HHV-8 serostatus and transfusion history and a Markov model to estimate the transmission risk per transfusion and the cumulative risk from community (i.e., nontransfusion) sources. Statistical tests were two-sided. RESULTS: HHV-8 antibodies were detected in 117 of 561 (21%) children with unambiguous K8.1 results. HHV-8 seroprevalence among the never-transfused children increased with age from 7% in children aged 0-2 years to 32% in those aged 13-16 years (P(trend)<.001). HHV-8 seropositivity was more frequent in transfused than never-transfused children (24% versus 17%, odds ratio = 1.48, 95% confidence interval [CI] = 0.97 to 2.26; P =.07). Seropositivity increased with number of reported transfusions, with age-adjusted odds ratios of 0.97 (95% CI = 0.54 to 1.75), 1.13 (95% CI = 0.59 to 2.17), 1.76 (95% CI = 0.81 to 3.83), and 2.17 (95% CI = 1.18 to 3.99) for children with one, two, three, or four or more transfusions, respectively (P(trend) =.007). Overall, the estimated HHV-8 transmission risk was 2.6% per transfusion (95% CI = 1.9% to 3.3%), whereas the annual risk of infection unrelated to transfusion was 2.7% (95% CI = 1.7% to 3.7%). CONCLUSION: Our study suggests that blood transfusion is associated with a small risk of HHV-8 transmission. In Uganda, this risk is approximately equivalent to the 1-year cumulative risk of infection from community sources.

Adolescent↗

PubMed2003

Malignant thymoma in the United States: demographic patterns in incidence and associations with subsequent malignancies.

The cause of thymoma is unknown. No population-based study has described demographic patterns of thymoma incidence. Previous reports have linked thymoma with diverse subsequent malignancies, but these associations are uncertain. We used Surveillance, Epidemiology and End Results (SEER) data to study the incidence of malignant thymoma by sex, age and race in the United States (1973-1998). Incidence was modeled with joinpoint regression (for age) and Poisson regression. We also used SEER data to compare malignancies following thymoma diagnosis with those expected from general population rates, calculating the standardized incidence ratio (SIR, observed/expected cases) to measure risk. The overall incidence of malignant thymoma was 0.15 per 100000 person-years (849 cases). Thymoma incidence increased into the 8th decade of age and then decreased. Incidence was higher in males than females (p=0.007) and was highest among Asians/Pacific Islanders (0.49 per 100000 person-years). Following thymoma, there were 66 malignancies (SIR 1.5, 95%CI 1.2-1.9). The most notable excess risk for subsequent malignancy was for non-Hodgkin's lymphoma (B immunophenotype) where the SIR was 4.7 (95%CI 1.9-9.6, 7 cases). There were also excess digestive system cancers (SIR 1.8, 95%CI 1.1-2.9) and soft tissue sarcomas (SIR 11.1, 1.3-40.1). No other cancers were increased after thymoma. In conclusion, malignant thymoma is extremely rare. The peak in late adulthood deserves further study. Variation in incidence by race suggests a role for genetic factors. Our study did not demonstrate broadly increased risk for malignancies following thymoma.

Adult↗

PubMed2003

Graphical methods for class prediction using dimension reduction techniques on DNA microarray data.

MOTIVATION: We introduce simple graphical classification and prediction tools for tumor status using gene-expression profiles. They are based on two dimension estimation techniques sliced average variance estimation (SAVE) and sliced inverse regression (SIR). Both SAVE and SIR are used to infer on the dimension of the classification problem and obtain linear combinations of genes that contain sufficient information to predict class membership, such as tumor type. Plots of the estimated directions as well as numerical thresholds estimated from the plots are used to predict tumor classes in cDNA microarrays and the performance of the class predictors is assessed by cross-validation. A microarray simulation study is carried out to compare the power and predictive accuracy of the two methods. RESULTS: The methods are applied to cDNA microarray data on BRCA1 and BRCA2 mutation carriers as well as sporadic tumors from Hedenfalk et al. (2001). All samples are correctly classified.

Algorithms↗

PubMed2003

Prevalence of SEN viruses among injection drug users in the San Francisco Bay area.

SEN viruses (SENVs) are newly discovered bloodborne viruses that may play a role in liver disease. SENV strain prevalence was examined in a race/ethnicity-stratified sample of 531 injection drug users (IDUs) from the San Francisco Bay area. Weighted prevalences were as follows: SENV-A, 45.7%; SENV-C/H, 35.6%; and SENV-D, 10.3%. Infection was associated with a longer duration of injection drug use. SENV-A was more common in black subjects (adjusted odds ratio [OR(a)], 4.37; 95% confidence interval [CI], 2.65-7.21) and Hispanic subjects (OR(a), 2.30; 95% CI, 1.38-3.85) than in white and non-Hispanic subjects, and the pattern was similar for SENV-C/H. For SENV-D, prevalence was similar in black and white subjects, but lower in Hispanic subjects; infection was less common among women than men (OR(a), 0.32; 95% CI, 0.15-0.71) and more common among men with at least 1 recent male sex partner than among heterosexual men (OR(a), 7.05; 95% CI, 2.62-18.95). SENV strains are common among San Francisco Bay area IDUs, and prevalence varies demographically within this group.

Adult↗

PubMed2003

Human herpesvirus 8 infection within families in rural Tanzania.

Human herpesvirus 8 (HHV-8) infection is common in Africa. We examined the distribution of HHV-8 within families in rural Tanzania to determine routes of spread. HHV-8 infection was assessed by measuring antibody reactivity with a K8.1 (lytic-phase antigen) immunoassay. The prevalence increased from 3.7% (1/27) among infants to 58.1% (36/62) among children aged 3-4 years and 89.0% (65/73) among adults aged > or =45 years. Women with HHV-8-seropositive husbands had a 7-fold risk for infection (odds ratio [OR], 6.9; 95% confidence interval [CI], 1.9-25.3). HHV-8 seropositivity in children was associated with having at least 1 seropositive first-degree relative (OR, 14.7; 95% CI, 5.9-43.1), a seropositive mother (OR, 7.4; 95% CI, 3.2-16.8), a seropositive father (OR, 4.8; 95% CI, 2.3-10.1), or a seropositive next-older sibling (OR, 4.2; 95% CI, 1.9-9.4). Our data are consistent with the occurrence of HHV-8 transmission within families, from mothers and other relatives to children via nonsexual routes and between spouses via sexual routes.

Adolescent↗

PubMed2003

Robustness of inference on measured covariates to misspecification of genetic random effects in family studies.

Family studies to identify disease-related genes frequently collect only families with multiple cases. It is often desirable to determine if risk factors that are known to influence disease risk in the general population also play a role in the study families. If so, these factors should be incorporated into the genetic analysis to control for confounding. Pfeiffer et al. [2001 Biometrika 88: 933-948] proposed a variance components or random effects model to account for common familial effects and for different genetic correlations among family members. After adjusting for ascertainment, they found maximum likelihood estimates of the measured exposure effects. Although it is appealing that this model accounts for genetic correlations as well as for the ascertainment of families, in order to perform an analysis one needs to specify the distribution of random genetic effects. The current work investigates the robustness of the proposed model with respect to various misspecifications of genetic random effects in simulations. When the true underlying genetic mechanism is polygenic with a small dominant component, or Mendelian with low allele frequency and penetrance, the effects of misspecification on the estimation of fixed effects in the model are negligible. The model is applied to data from a family study on nasopharyngeal carcinoma in Taiwan.

Analysis of Variance↗

PubMed2003

Sample size calculations for population- and family-based case-control association studies on marker genotypes.

Most previous sample size calculations for case-control studies to detect genetic associations with disease assumed that the disease gene locus is known, whereas, in fact, markers are used. We calculated sample sizes for unmatched case-control and sibling case-control studies to detect an association between a biallelic marker and a disease governed by a putative biallelic disease locus. Required sample sizes increase with increasing discrepancy between the marker and disease allele frequencies, and with less-than-maximal linkage disequilibrium between the marker and disease alleles. Qualitatively similar results were found for studies of parent offspring triads based on the transmission disequilibrium test (Abel and Müller-Myhsok, 1998, Am. J. Hum. Genet. 63:664-667; Tu and Whittemore, 1999, Am. J. Hum. Genet. 64:641-649). We also studied other factors affecting required sample size, including attributable risk for the disease allele, inheritance mechanism, disease prevalence, and for sibling case-control designs, extragenetic familial aggregation of disease and recombination. The large sample-size requirements represent a formidable challenge to studies of this type.

Alleles↗

PubMed2002

Two approaches to mutation detection based on functional data.

A new technique, denaturing high-performance liquid chromatography (dHPLC), allows for detection of any heterozygous sequence variation in a gene without prior knowledge of the precise location of the sequence change. The results of a dHPLC analysis are recorded in real-time in the form of a chromatogram that is sequence-specific. In this paper we present methods to classify an individual, based on the observed chromatogram, as a homozygous wild-type or a carrier of a specific variant for the given DNA segment by comparison to representative chromatograms that are obtained from the training set of individuals with known variant status. The first approach consists of finding a parsimonious parametric model and then classifying each newly observed curve based on comparing the most discriminating characteristic, the main mode, to the main mode of the training curves. The second approach consists of finding empirical estimates of the modes of each chromatogram and using a bootstrap test for equality with the corresponding estimates of the training curves. We apply both methods to data on the breast cancer susceptibility gene BRCA1 and test the performance of the methods on independent samples.

Algorithms↗

PubMed2002

Efficiency of DNA pooling to estimate joint allele frequencies and measure linkage disequilibrium.

Pooling DNA samples can yield efficient estimates of the prevalence of genetic variants. We extend methods of analyzing pooled DNA samples to estimate the joint prevalence of variants at two or more loci. If one has a sample from the general population, one can adapt the method for joint prevalence estimation to estimate allele frequencies and D, the measure of linkage disequilibrium. The parameter D is fundamental in population genetics and in determining the power of association studies. In addition, joint allelic prevalences can be used in case-control studies to estimate the relative risks of disease from joint exposures to the genetic variants. Our methods allow for imperfect assay sensitivity and specificity. The expected savings in numbers of assays required when pooling is utilized compared to individual testing are quantified.

Alleles↗